a simple explanation of why so many economists are so often surprised (by “trends” AKA “fat tails”)

a simple explanation of economic “surprises”

Image via Wikipedia

Most university-trained economists apparently do not have any practical understanding of the simplest realities of economics. Consider how frequently university-trained economists seem to be surprised by what people like myself easily predict. They scramble to explain developments after being surprised by them, evidencing what I consider no practical competence in the field. They may compile statistics that can quantify what happened weeks and months ago, but to what practical value?

The only university in the US that has a program in economics that systematically emphasizes anything even close to what I consider practical economics, to the best of my knowledge, is Auburn in the state of Alabama. Politically, a certain range of economic thought is totally dominant within US academia (and worldwide, I presume). However, economic models that do not actually predict economic activity are like physics models that do not predict whether or not water will boil at a certain temperature (and air pressure). Or, think of a 4-year program to learn to converse in the English language, after which, none of the participants can understand the English spoken in a movie for children… unless there are subtitles that spell out even the simplest English words. That is not much competence, right? That is like university-training in economics, to me, or maybe even in psychology.

Economics (Photo credit: markwainwright)

I could give you a specific example in the realm of finance in particular. Business schools worldwide teach a model that is popular but distinctively misleading (and costly).

I’m using some statistics terminology that I presume is familiar to you. Perhaps you have heard of “the bell curve.” It is a statistical reference to an idealized random distribution. It is used routinely in finance and economics. While it is mathematically convenient, there is a serious problem with using it: it does not fit with the reality of economic and financial data variance.

Economic data is not random, but orderly and patterned, so economic and financial data does not conform to the idealized mathematical bell curve. The bell curve is useful for establishing how far away from random a particular data set is. It is extremely expensive, however, to know that economic data is not random- and have clearly measurements of exactly how far away from random economic data is- but then ignore that orderliness and use models based on a random bell curve to calculate probabilities.

That is exactly how so many corporations and banks and governments are in such serious trouble today- because they relied on inaccurate models promoted in mainstream academic economics. Further, university-trained economists may imply or even directly tell people that economic phenomenon is random, which is objectively false. Their popular economic models do not predict a significant and regular portion of data.

Again, as an analogy from astronomy: the retrograde motion of planets. Are you familiar with that?

When various Europeans from Archimedes to Copernicus and Galileo noticed that everything except the sun and moon have regular periods of retrograde motion, they suggested that the earth-centered model of planetary motion is obviously inaccurate and that the data clearly establish that the planets revolve around the sun, rather than around the earth. As you may know, various scientists were threatened, tortured or killed for publicizing the accuracy of the sun-centered model which conflicted with the earth-centered model authorized by the Holy Roman Empire.

So, the same thing applies to economics in academia. The truth is simple enough, but it is politically unacceptable to many in positions of influence, such as in academia. Consider that I have also just said quite a bit about psychology, and I am implying that most people in universities may not know much about psychology, or at least certain psychological phenomenon and predictable patterns (which gets into quantitative analysis AKA statistics AKA math).

Image via Wikipedia

The image above is the ideal of statistical randomness called a “bell curve,” because it is shaped like a bell. This is purely a mathematical model, not based on observation of real “natural” phenomenon like annual rainfall or the daily closing price of gold. (I classify humans as part of nature, by the way.)

Of course, there are actual “data sets” that do correspond to this curve, but science is about the modeling of “cause-effect correlations” that do not fit this curve! This curve displays what is meant by the term “statistically insignificant” as distinct from “statistically significant,” like the relationship between age and height amongst the youth of a particular species, like human children.

Most phenomenon in nature are related to some other variable (a catalyst, cause, or correlate), and the correlation is measured against that ideal “random” bell curve. For instance, the above curve is an idealized shape generated by such experiments as “an infinite number of coin tosses” with the two flat ends of the curve representing the frequency of various sequences of “heads in a row” or “tails in a row.” Investment markets (which measure- as in count- the investing behaviors of humans) clearly evidence a complex matrix of dependencies, not simple randomness.

In this other image with many colors, the black line is the “normal” or random bell curve. However, economic and financial data conform more to the range of the green, orange, and red lines: with much more data concentrated toward the center range of very small variations (such as daily variations of price in a stock market), but also a high frequency of data that is MUCH higher or WAY lower than the typical or “median” range of data. (The center of the “bell” can be contrasted with the sides or “tails” and for non-random data that trends- as in all data from nature- there are “fat tails” – and some fat tails are fatter than others, like commodity market pricing such as for silver tend to be measurably much more volatile than currency markets or bond markets.)

In other words, economic and financial data are not random, but adhere to tight patterns most of the time, and also have “surprisingly” frequent erratic variations (far away from the tight common range). That is, economic and financial data are data that trend (that move in trends), but then occasionally “break trend” or reverse trend- and I would add that they very predictably and very regularly break trend- and far more often than “random probability.”

That basic insight of the existence of trends, oddly enough, is not widely accepted (or integrated as relevant) in mainstream academic economics, to the best of my knowledge. It is why so many economists can all be wrong at once- because they are all using the same over-generalized model which systematically and regularly under-estimates certain regular patterns of phenomenon (“risk”)… in favor of a model that is mathematically convenient but does not fit the actual phenomenon. Yes, they systematically utilize the inaccurate model (of imaginary random statistics), ignoring the actual data (observed orderly statistics).

English: Crown of the Holy Roman Empire. (Photo credit: Wikipedia)

In the real world of finances and investments, lots of companies can go bankrupt together (as each defaulting business means that others will not get paid money that they were legally owed and may have enthusiastically been expecting)- or an entire industry can boom suddenly. The conclusion of a lawsuit or a change of the law can also produce big reactions in investing behavior, as can a declaration of war or a terrorist attack.

But even beyond all of those obvious “unusual” factors, economic data clearly trend. When trends break, they often do so dramatically- which produces something known as “the snowball effect.” Do you know that term? It is all about herding, mania, hysteria, and panic.

Even without any obvious “trigger” from politics or technology, economic trends occasionally reverse. Buying trends can catch on very suddenly, just like trends in fashion. In fact, fashion trends are one form of buying trend. The point is that the more people that start a new behavior in a trend, the more that other people see someone else doing that new behavior, so the frequency can rise exponentially, as in the following chart:

first month- 20 cases (if each of those 20 people tell 19 people…)
second month- 400 cases (and if each of those 400 people tell 399 people…)
third month- 64000 cases (and so on….)
fourth month- many millions of cases
fifth month- 0 people

That last thing is the big one. Fashion trends can end even faster than they develop, right? Once everyone else is doing it too, it is no longer “cool.” That reminds of Dr. Seuss and the Star-bellied Sneetches.

The same is true of outbreaks of pandemics. They can grow exponentially, accelerating faster and faster over time. Wildfires can grow exponentially, too, but then once the fuel is burned out, the fire suddenly ends.

Growth is not random. Nothing in life is random, including fashion trends and other economic patterns.

Think of how public popularity of a celebrity can grow in huge surges, then totally disappear. For instance, OJ Simpson or Tiger Woods or Jimmy Swaggart can be an increasingly popular actor for rental-car advertising or whatever, month after month doubling in popularity regularly… but then all of those ads suddenly get pulled because of some scandal or accusation or just one well-developed photo involving a prostitute.

Similarly, considering something like the manufacturing and selling of typewriters, the use of typewriters grew exponentially for decades, then dropped dramatically and suddenly when overtaken by computers,when computers became popular and cheap for word processing. Decade after decade, typewriters were getting more and more popular, then suddenly they were nearly forgotten- like DVDs have demolished the market for VHS tapes, which previously demolished the market for Beta-max videos.

A portrait of Karl Marx. (Photo credit: Wikipedia)

Economics is never, ever random! Economic patterns often grow exponentially and can change very suddenly. Models that may be popular in academia and that pretend that economics is not orderly but random… can be very expensive. By the way, the actual main obvious error in particular in global economics recently is that the easy credit bubble had been inflating exponentially… and then suddenly stopped growing (and started to suddenly deflate). See https://jrfibonacci.wordpress.com/inflation-deflation-and-credit/

So, for people who are economists (and accountants) to ignore the reality of economics in favor of a theory of randomness and then to actually pretend that economic patterns are random when they are obviously not… is simply idiotic, however popular in mainstream academia. Popularity or public notoriety is not a measure of precision or utility.

The earth-centered theory of astronomy was popular and even very useful for MOST astronomical occurences, but was absolutely wrong if taken literally. Worshiping a model over reality, even when done by Holy Roman Emperors, is sin, foolishness, pure vanity, and often very expensive, too.

Isaiah 29:13Then the Lord said, “Because this people draw near with their words And honor Me with their lip service, But they remove their hearts far from Me, And their reverence for Me consists of tradition learned by rote…

[rather than from direct experience?]

14 Because of this, I will once again astound these [naively proud] hypocrites with amazing wonders [surprises!]. The wisdom of the wise will pass away, and the intelligence of the intelligent will disappear.”

So, the terms “random walk” and “efficient market” theory are references to over-simplistic models of economic phenomenon as random. You may know those terms from college classes. Yes, they are part of the history of academic economics, however, they are obsolete models: misleading and expensive to use because they GROSSLY under-estimate risk (as well as opportunity).

Now, in a twist that may not surprise you, trending or herding is a psychological phenomenon. In other words, economics is a subcategory of psychology.

Psychology, to me, is a study in patterns of behavior. Economics is the study of a certain range of behavior, such as buying and selling and borrowing. However, economics also explains why subsistence farmers choose certain crops and farming methods. Economics, more generally, is the study of prioritizing, as in the prioritizing of various alternatives of action. I assert to you that many people in academic economics would not give that simple and obvious of a definition. If you asked them to explain to a 6 year-old what economics is, they might find that very challenging.

Economics is why people wear rain coats outside on cloudy days, but do not wear earmuffs and mittens on hot days- unless they are dressing up for fun. “Austrian Economics” at least is about utilitarian selection of various alternatives- and is much more fundamental than the human realms of law or accounting, which are themselves just sub-systems of economics. (The hierarchy is clarified here: https://jrfibonacci.wordpress.com/2010/06/30/above-average-intelligence/ )

So, most academic economists apparently cannot even predict the simplest and biggest financial developments in advance. However, they are just academics, right? That means they are in the mode of trying to develop and assess theories. Further, they can only fully assess something they can understand.

That is why someone like Mandelbrot (or John Nash, featured in the movie “A Beautiful Mind”) was nearly universally dismissed at first. Each was so far ahead of his contemporaries that it took decades for mainstream academics to acknowledge and apply their work. Mandelbrot’s extensive analysis of the fractal patterns in the orderly variations of cotton prices was generally ignored for decades. Today, almost every movie with CGI graphics applies his math. Before computers, most mathematicians were incapable of comprehending most of what he was observing and modeling. As for Nash, his mathematical modeling of “non-rational herding behavior” (which “dove-tails” with the work of Mandelbrot) is perfect for calculating trends and risk, but of all the institutions that are facing bankruptcy today, how many of them used accurate models of risk? None!

Speculators in the so-called “real world” of business and real estate and stocks and government bonds and so on always get the results that fit with the accuracy of the model they are using. Irrelevant models always fail eventually.

Accurate models always prevail eventually. Or, maybe the Holy Roman Empire was right and the sun revolves around the earth. I wouldn’t bet on such obviously inaccurate models, but many investors do. That makes it incredibly easy for some of the rest of us.